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Memorias de investigación
Communications at congresses:
Automatically Modeling Hybrid Evolutionary Algorithms from Past Executions
Year:2010
Research Areas
  • Artificial intelligence
Information
Abstract
The selection of the most appropriate Evolutionary Algorithm for a given optimization problem is a difficult task. Hybrid Evolutionary Algorithms are a promising alternative to deal with this problem. By means of the combination of different heuristic optimization approaches, it is possible to profit from the benefits of the best approach, avoiding the limitations of the others. Nowadays, there is an active research in the design of dynamic or adaptive hybrid algorithms. However, little research has been done in the automatic learning of the best hybridization strategy. This paper proposes a mechanism to learn a strategy based on the analysis of the results from past executions. The proposed algorithm has been evaluated on a well-known benchmark on continuous optimization. The obtained results suggest that the proposed approach is able to learn very promising hybridization strategies.
International
Si
Congress
EvoApplications 2010
960
Place
Reviewers
Si
ISBN/ISSN
978-3-642-12238-5
Start Date
07/04/2010
End Date
09/04/2010
From page
422
To page
431
Automatically Modeling Hybrid Evolutionary Algorithms from Past Executions
Participants
  • Autor: Santiago Muelas Pascual (UPM)
  • Autor: Jose Maria Peña Sanchez (UPM)
  • Autor: Antonio Latorre De la Fuente (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
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